Classification assigns labels to data based upon a decision rule. A convex surrogate loss function is used as the training loss function in many classification procedures. Within the statistical learning community, convex surrogates are preferred because of the virtues that convexity brings—unique optimum, efficient optimization using convex optimization tools, amenability to theoretical analysis of error bounds, etc. However, convex functions are poor approximations for a wide variety of problems.